路側カメラによる自動運転向けリアルタイム全スタック交通シーン認識
Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras
路側カメラを用いて物体検出・位置推定・追跡・多カメラ融合を統合し、2Dアノテーションのみで学習可能な landmark ベース3D位置推定を導入したフルスタック認識システムを構築し、実環境で20ms未満の遅延を実現した。
著者: Zhengxia Zou, Rusheng Zhang, Shengyin Shen, Gaurav Pandey, Punarjay Chakravarty, Armin Parchami, Henry X. Liu
分類: cs.CV, cs.RO
原文アブストラクト
We propose a novel and pragmatic framework for traffic scene perception with roadside cameras. The proposed framework covers a full-stack of roadside perception pipeline for infrastructure-assisted autonomous driving, including object detection, object localization, object tracking, and multi-camera information fusion. Unlike previous vision-based perception frameworks rely upon depth offset or 3D annotation at training, we adopt a modular decoupling design and introduce a landmark-based 3D localization method, where the detection and localization can be well decoupled so that the model can be easily trained based on only 2D annotations. The proposed framework applies to either optical or thermal cameras with pinhole or fish-eye lenses. Our framework is deployed at a two-lane roundabout located at Ellsworth Rd. and State St., Ann Arbor, MI, USA, providing 7x24 real-time traffic flow monitoring and high-precision vehicle trajectory extraction. The whole system runs efficiently on a low-power edge computing device with all-component end-to-end delay of less than 20ms.
